Proven Best Practices for Data Quality Management in Modern Enterprises

Data has become the cornerstone of operational efficiency, decision-making, and digital transformation. However, poor-quality data leads to missed opportunities, process inefficiencies, and increased costs. That’s why implementing best practices for data quality management (DQM) is no longer optional—it’s essential.

At the heart of effective DQM is a well-defined governance framework, starting with establishing roles like data owners and data stewards. Companies must focus on standardizing data collection methods, cleansing legacy data, and continuously monitoring for errors and inconsistencies.

According to PiLog’s insightful article, businesses should adopt a holistic approach combining people, process, and technology. This includes automated data profiling, validation rules, and AI-powered tools for enrichment. Most importantly, data quality is not a one-time project—it’s a continuous process that evolves with business needs.

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